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August 11, 2016Physiological Measurement

T-wave morphology parameters enabled 90% discrimination between control and LQTS patients in the normal QTc subgroup, compared to 71% using QTc alone.

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Why the study?

Does automated analysis of T-wave morphology improve discrimination between healthy controls and LQTS patients compared to QTc alone?

Population

Healthy controls and Long QT syndrome patients, including a subgroup with normal QT intervals from the…

Comparison

Automated analysis of T-wave morphology from… vs Classification based on QTc alone.

Design

Cross-sectional

Authors

SISarah A. ImmanuelASArash SadriehMBMathias Baumert

Discussion

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Overview

May aid risk stratification in genotype-positive LQTS with normal QTc; hypothesis-generating and requires prospective validation.

Structured PICO

Does automated analysis of T-wave morphology improve discrimination between healthy controls and LQTS patients compared to QTc alone?

P
Population
Healthy controls and Long QT syndrome (LQTS) patients, including a subgroup with normal QT intervals (400-450 ms, 67 controls and 61 LQTS) from the Telemetric and Holter ECG Warehouse (THEW) database.
I
Intervention
Automated analysis of T-wave morphology from Holter ECG recordings using neural network classifiers based on sigmoid or polynomial fits.
C
Comparator
Classification based on QTc alone.
O
Outcome
Discrimination between control and LQTS patients.surrogate

Automated analysis of T-wave morphology from Holter ECGs can accurately distinguish LQTS patients from healthy controls, even when QTc intervals are within the normal range.

Cite This Study

Immanuel et al. (2016) studied this question.

synapsesocial.com/papers/6a70b478f44fa9f079de64cdhttps://doi.org/10.1088/0967-3334/37/9/1456
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Identification of Concealed and Manifest Long QT Syndrome Using a Novel T Wave Analysis Program2016 · 28 citations
  2. 2Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis2018 · 30 citations
  3. 3Differentiating long QT syndrome genotypes using electrocardiographic geometric parameterization and machine learning approaches2026
  4. 4Software-based analysis of T-wave morphology: identifying the electrocardiogram signature of high-risk long QT syndrome2025
  5. 5The diagnostic role of T wave morphology biomarkers in congenital and acquired long QT syndrome: A systematic review2022 · 17 citations